Overview

Project summary

Goals:

Findings:

Data Overview

Twitter data

Twitter data was obtained freely through a partnership between UCSB Library and Crimson Hexagon. Before downloading, the data was queried to meet the following conditions:

  1. Tweet came from the Santa Barbara area (add more details about how CH applies the location query)
  2. Only original tweets (no retweets)
  3. Date was marked between January 1, 2015 and December 31, 2019

Crimson Hexagon only allows 10,000 randomly selected tweets to be exported, manually, at a time in .xls format. Due to this restriction, data was manually downloaded for every 2 days in order to capture all tweets. There were around 5000 average number of daily tweets that met these conditions.

The Crimson Hexagon data did not contain all desired information, including whether or not the tweet was geotagged. To get this information we used the python twarc library to “rehydrate” the data using individual tweet ids and store the tweet information as .json files. From here we were able to remove all tweets that did not have a geotag, giving us a total of 82,876 tweets.

Table of data

Here is a sample of the type of the final twitter information we obtained.

created_at tweet_id full_text user_id user_location geo_type geo_coordinates language retweet_count favorite_count lat lon
Thu Mar 19 06:42:44 +0000 2015 5.784465e+17 You don’t become obsessed with money until you get a job 2381122279 Oxnard, CA Point c(34.46758064, -119.75160822) en 0 1 34.46758 -119.7516
Sat Oct 28 19:11:57 +0000 2017 9.243531e+17 Adorable! Happy fall from goletafloral who made this for me for the salon. I love surprises… https://t.co/VA82T9erlQ 204506968 Santa Barbara, CA Point c(34.4348412, -119.8198929) en 0 0 34.43484 -119.8199
Sun Mar 22 05:54:52 +0000 2015 5.795216e+17 @Thomas_lundyyy true true😂find a new friend 189565102 13/9/13 r.i.p granny ily xxx Point c(34.44111088, -119.74887975) en 0 0 34.44111 -119.7489
Wed Mar 23 00:05:21 +0000 2016 7.124300e+17 Amazing cuisine comes out of @thelarksb kitchen. It’s no wonder with such an amazing kitchen… https://t.co/j0f6KYxJpd 1368498818 Santa Barbara Point c(34.41492371, -119.69080081) en 1 2 34.41492 -119.6908
Tue Mar 10 06:04:35 +0000 2015 5.751754e+17 My candles, my jazz, and good conversation ❤️ #goodnight 39122583 San Francisco, CA Point c(34.41186981, -119.8548513) en 0 0 34.41187 -119.8549
Mon Nov 30 00:08:51 +0000 2015 6.711187e+17 Someone put some origami cranes on these trees that fell from the cliffs #nomads in #California @… https://t.co/rJZIYZlwAN 23234672 Santa Barbara, CA Point c(34.40295, -119.7439666) en 0 0 34.40295 -119.7440
Tue Mar 17 00:29:01 +0000 2015 5.776277e+17 lmao one of my housemates surprised visited her and we had to hide the all our bongs and hookah asap 😂 422088295 in my bag Point c(34.41379288, -119.86042301) en 0 0 34.41379 -119.8604
Thu Sep 01 19:33:07 +0000 2016 7.714307e+17 Starting tomrrow we are giving away a free Mizu Life water bottle with every bag purchased in… https://t.co/T77G4PUsYW 283395195 528 anacapa Santa Barbara Point c(34.42232, -119.70346) en 0 0 34.42232 -119.7035
Tue Jul 21 03:10:50 +0000 2015 6.233293e+17 california, i’ll miss you @ Lucky Penny Santa Barbara https://t.co/QL5Q3i8Rfs 783191605 NA Point c(34.4145889, -119.6905289) en 0 0 34.41459 -119.6905
Sun Oct 16 21:35:22 +0000 2016 7.877689e+17 Every Sunday you can shop the #StabilesMobile 🚚 on Canon Perdido + State St from 10-5pm go check… https://t.co/MEZqyobP4y 1668713359 Santa Barbara, California Point c(34.4204099, -119.70094) en 0 0 34.42041 -119.7009

The spatial distribution of tweets highlights areas of higher population density and tourist areas in downtown Santa Barbara. There is a single coordinate that has over 11,000 tweets reported across all years. It is near De La Vina between Islay and Valerio. There is nothing remarkable about this site so I assume it is the default coordinate when people tag “Santa Barbara” generally. The coordinate is 34.4258, -119.714.

Interactive with cluster markers

As you zoom in on the map, clusters will disaggregate. You can click on blue points to see the tweet.

Tweet density

This is log-transformed.

Identifying tourists and locals

This project aims to understand if and how preferences differ between tourists and locals for nature-based places within the Santa Barbara area. In order to test this we needed to come up with a way to identify tourists or locals. We used a two step process.

First, if the user has self-identified their location as somewhere in the Santa Barbara area, they are designated a local. This includes Carpinteria, Santa Barbara, Montecito, Goleta, Gaviota and UCSB. For the remainder, we use the number of times they have tweeted from Santa Barbara within a year to designate user type. If someone has tweeted across more than 2 months in the same year from Santa Barbara, they are identified as a local. This is consistent with how Eric Fischer determined tourists in his work. This is not fool-proof and there are instances were people visit and tweet from Santa Barbara more than two months a year, especially if they are visiting family or live within a couple hours driving distance.

There are 26408 tweets from tourists and 56468 tweets from locals.

The following map shows tweet log density by locals (top - blue) and tourists (bottom - red).

Identifying nature-based tweets

The full text of each tweet was analyzed to be either nature-based or not. We developed a coarse dictionary of words that indicate a nature-based tweet. These include natural features like ocean, coast, park, and works that indicate recreating (fishing, hiking, camping, etc.).

Note I had a hard time finding an ontology or lexicon that would fit this project. These are definitely skewed more towards nature and recreation rather than words like “home” or “connection”.

##  [1] "hike"        "trail"       "hiking"      "camping"     "tent"       
##  [6] "climb"       "summit"      "fishing"     "sail"        "sailing"    
## [11] "boat"        "boating"     "ship"        "cruise"      "cruising"   
## [16] "bike"        "biking"      "dive"        "diving"      "surf"       
## [21] "surfing"     "paddle"      "swim"        "ocean"       "beach"      
## [26] "^sea"        "sand"        "coast"       "island"      "wave"       
## [31] "fish"        "whale"       "dolphin"     "pacific"     "crab"       
## [36] "lobster"     "water"       "shore"       "marine"      "seawater"   
## [41] "lagoon"      "slough"      "saltwater"   "underwater"  "tide"       
## [46] "aquatic"     "^tree"       "^earth"      "weather"     "sunset"     
## [51] "sunrise"     "^sun"        "climate"     "park"        "wildlife"   
## [56] "^view"       "habitat"     "^rock"       "nature"      "mountains"  
## [61] "^peak"       "canyon"      "pier"        "wharf"       "environment"
## [66] "ecosystem"

Let’s look at some examples of what tweets qualified as “nature-based”.

Month Day Time Year full_text user_id user_location geo_coordinates retweet_count favorite_count lat lon month_num date user_type nature_word
Jun 12 18:30:00 2016 @ebbtideapp tide in santa barbara, california 06/13/2016 high 5:56pm 4.6 low 12:29am 2.1 high 5:39am 3.4 low 11:35am 1.4 4.754740e+09 Seattle, WA c(34.4083, -119.685) 0 0 34.40830 -119.6850 6 2016-06-12 local 1
Feb 6 01:44:53 2015 christmas miracle at santa claus beach. #sunsetmedicine @ santa clause state beach http://t.co/vzilnosk0l 9.506682e+06 portland, oregon usa c(34.41572394, -119.55948268) 0 0 34.41572 -119.5595 2 2015-02-06 tourist 1
Mar 24 22:36:03 2018 #familyday at the beach @ carpinteria state beach https://t.co/njiahaip8x 3.700721e+07 Long Beach, CA c(34.39330488, -119.52182365) 0 0 34.39330 -119.5218 3 2018-03-24 tourist 1
Feb 23 01:00:36 2015 watching shutter island for the first time…holy shit 3.836257e+08 Cali c(34.4516667, -119.6613954) 0 0 34.45167 -119.6614 2 2015-02-23 local 1
Sep 13 17:55:18 2018 waiting for us in the window #miathedog #yorkiesofinstagram #howmuchisthatdoggyinthewindow @ padaro beach grill https://t.co/nv5uusecht 9.236533e+08 NA c(34.4065895, -119.5483398) 0 1 34.40659 -119.5483 9 2018-09-13 tourist 1
Nov 8 21:27:41 2015 unpleasant af. @ boathouse at hendry’s beach https://t.co/0ppwj5mj6i 2.342645e+08 Los Angeles, California c(34.4035492, -119.7438889) 0 0 34.40355 -119.7439 11 2015-11-08 tourist 1
Nov 19 04:58:58 2018 so much horrible news these days. but then pops sends this pic with a note “we now own a red door!” and wearing the absolute raddest jacket 🙌🏼 #hero #livingthebestlife #waterwitching #dads… https://t.co/iclqomhmcs 2.876582e+09 Ventura, CA c(34.40926694, -119.69799817) 0 0 34.40927 -119.6980 11 2018-11-19 tourist 1
May 9 19:07:36 2016 post-surf breakie with my girls. @ crushcakes and simply pies https://t.co/lucacfbnxc 1.531541e+07 Santa Barbara, CA c(34.43528747, -119.81225647) 0 0 34.43529 -119.8123 5 2016-05-09 local 1
Jul 25 21:08:14 2017 a fun park to visit in goleta climb on a dinosaur! #santabarbara #sb #805families #805parks… https://t.co/vx88zceop1 7.728943e+17 Ventura, CA c(34.41273055, -119.86892952) 0 0 34.41273 -119.8689 7 2017-07-25 local 1
Feb 22 20:47:30 2016 #sunset by the #pier in #santabarbara 🍷🌅 #beach #oceanview. #vacation #roadtrip along the… https://t.co/jla14lbcsz 3.357299e+07 New York, USA c(34.4258, -119.714) 0 0 34.42580 -119.7140 2 2016-02-22 tourist 1
Nov 12 08:29:17 2018 the yes store would like to welcome the return of watercolor artist carissa luminess.    carissa joie luminess (nee erin williams) enjoyed a well-rounded bi-coastal upbringing between… https://t.co/x685qpehjf 1.721485e+07 Santa Barbara, CA c(34.41957116, -119.70044374) 0 2 34.41957 -119.7004 11 2018-11-12 local 1
Sep 1 20:45:17 2018 goleta’s city hall is in the same business park as our new offices. https://t.co/ke2fj1zpmx 5.780652e+06 Seattle → Bangkok → SoCal c(34.4329834, -119.85419048) 0 3 34.43298 -119.8542 9 2018-09-01 local 1
Nov 29 20:59:32 2019 quick trip to drop brock off, the. to santa barbara to pick up an early 1900’s stradivara (known for tone) cabinet. it’s beautiful. the new project will give it a new life and new purpose #lovethecoast #santabarbara… https://t.co/4wcy930zfy 3.021694e+08 Bakersfield, CA c(34.40898406, -119.5520469) 0 0 34.40898 -119.5520 11 2019-11-29 tourist 1
Jan 10 18:06:20 2015 loki & norman had a fun play date this morning #puppydate #summerland @ summerland beach http://t.co/yybldojc8d 2.442781e+09 Santa Barbara, Ca. c(34.42017729, -119.60185812) 0 0 34.42018 -119.6019 1 2015-01-10 local 1
Dec 31 04:57:05 2017 double dolphin sunset adventure in #santabarbara #california 🐬🐬 #nofilter oracrest @ santa… https://t.co/qswl6u6kvi 6.521364e+07 Austin, TX c(34.40379, -119.69359) 0 0 34.40379 -119.6936 12 2017-12-31 tourist 1
Mar 2 14:18:24 2017 yesterday we went on a hike! the view was amazing 💍🌎 picture made by our new friend:… https://t.co/auasnu81wb 5.118252e+08 Hilversum, The Netherlands c(34.42441095, -119.70927001) 0 13 34.42441 -119.7093 3 2017-03-02 tourist 1
Feb 18 15:14:40 2016 seattle , seattle, i’m on my way! it was mentioned that many of you missed my first post about… https://t.co/xj4zmchi7m 1.825978e+07 Santa Barbara, CA c(34.42962349, -119.71646896) 0 0 34.42962 -119.7165 2 2016-02-18 local 1
Apr 5 23:27:43 2016 may have made ian drive onto the wharf which is in the middle of the ocean. yay piers! (@ stearns wharf) https://t.co/x30snwiobv 8.770602e+08 Sweet Home Chicago c(34.40979971, -119.68551993) 0 0 34.40980 -119.6855 4 2016-04-05 tourist 1
Oct 26 00:16:25 2017 the sea will forever be my church💙 #life #beach #ocean #montecito #love #surf #saltandsea #sea… https://t.co/7qlkaqmhcf 2.719479e+07 Agoura Hills c(34.419087, -119.631728) 0 0 34.41909 -119.6317 10 2017-10-26 tourist 1
May 11 23:52:57 2015 i am fascinated with coastlines. the vibe they give changes with each lookout & continent you stand… https://t.co/ybtp6g4sxi 2.048390e+08 LA c(34.39210089, -119.52026845) 0 0 34.39210 -119.5203 5 2015-05-11 tourist 1

Where are nature-based tweets?

After identifying nature-based tweets we can take a look at where these tweets are coming from and compare to the general pattern of tweets.

Who is tweeting nature-based tweets?

Not surprisingly there are less nature-based tweets than nature-based. Of all tweets, % are nature-based.

Of local tweeters, 13.7643962% of tweets are nature-based. Of tourists, 21.716904% are nature-based.

Are tweets in protected areas more often nature-based?

California Protected Areas Database

We can use the CPAD data to identify protected areas. [expandon CPAD here]

How many tweets come from these areas?

Count how many points in each polygon (all types of tweets not just nature based)

Some of these areas overlap with high tourist areas (e.g. the Bowl, Zoo and Wharf). Let’s look at the content of these tweets to see how often tweets coming from these locations are nature-based or not.

Compare occurrence of nature vs non-nature based tweets

There is no site with less than 50% of tweets being nature-based.

The highest ratio of nature tweets to non-nature takes place at Manning Park.

Let’s look at the top 20 most popular sites

Not surprisingly the Santa Barbara Bowl has the most number of tweets, but only half are nature based (the view is great!). If we just look at proportion of nature-based tweets we see a different ordering. I removed any places with just 1 tweet since it will skew results if that tweet happens to be nature-based (a total of 4 places).

How does this differ across tourists and locals?

Looking at the breakdown between tourists and locals. The sites included here have at least 50 tweets total across the time frame.

Are there areas with >50% nature-based tweets that aren’t designated?

Time

Timeline of tweets

Initial hypothesis was identifying spikes in nature-based tweets around three significant events: - Refugio oil spill in 2015 - Thomas fire in 2017 - Debris flow in 2018

Word clouds

top 100 words for locals vs tourist. And we could do this in space. At sterns wharf what are people tweeting about? At Elings, what are locals tweeting about?

Maybe in word clouds we can see some changes due to natural events

All of SB

By area

Sentiment Analysis

Lessons learned

Data is harder to find

Future research

Looking at different scale areas

There might be an interesting comparison between rural-suburban-urban areas. We hypothseize that the tourist/local alignment would split in urban areas, maybe aligned in suburban (like SB) and maybe not exist in rural.

Proportion of words that are nature based tells you how people. In Santa Barbara, there will be a lot of nature-based sense of place. In Manhattan, we wouldn’t expect to see nature based ones so much.

In a blog piece we can pose questions that we couldn’t answer but stuff like “can proportion of tourists/locals in place engagement tell us anything”.

Could compare % nature based tweets in SB to other areas. If we did this across the whole state, what proportion% are nature based? Maybe on average its just 5%.

Where and why do locals and tourists overlap in their use of area. SB seems to have a high alignment of tourists/locals, which may be helpful for local policy. Maybe places with distinct differences in how tourists/locals use places.

Look at cities of different coastal sizes rural - small town - urban - mega city. Could see how tourists/locals patterns differentiate across scale.

Is there a threshold of tourists where locals don’t go anymore?

In areas where we see both tourists and locals engaging, what characteristics do we see?

Quantifying transitions between rural to city.

Talk about overall social media literature for conservation and how this project is similar/different and used lessons from those papers to guide this analysis.